Reconstructed Slides: How fast are LLM inference engines anyway? — Charles Frye, Modal

Source Video

How fast are LLM inference engines anyway? — Charles Frye, Modal

Method

This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

Reconstructed Slides

• Source frame: slide-001.jpg
• Crop: full [0, 0, 960, 540] score 160.12

• Source frame: slide-002.jpg
• Crop: full [0, 0, 960, 540] score 166.1

• Source frame: slide-003.jpg
• Crop: full [0, 0, 960, 540] score 166.89

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 540] score 162.14

• Source frame: slide-005.jpg
• Crop: full [0, 0, 960, 540] score 159.38

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 540] score 176.23

• Source frame: slide-007.jpg
• Crop: full [0, 0, 960, 540] score 176.58

• Source frame: slide-008.jpg
• Crop: full [0, 0, 960, 540] score 162.74

Dense Scene-Detected Slide Candidates

• youtube DeFF3J8T5Pk dense slides